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Summary: Probabilistic vs. Geometric Similarity Measures for Image Retrieval
Selim Aksoy and Robert M. Haralick
Department of Electrical Engineering
University of Washington
Seattle, WA 98195-2500
{aksoy,haralick}@isl.ee.washington.edu
Abstract
Similarity between images in image retrieval is measured
by computing distances between feature vectors. This pa-
per presents a probabilistic approach and describes two
likelihood-based similarity measures for image retrieval.
Popular distance measures like the Euclidean distance im-
plicitly assign more weighting to features with large ranges
than those with small ranges. First, we discuss the ef-
fects of five feature normalization methods on retrieval per-
formance. Then, we show that the probabilistic methods
perform significantly better than geometric approaches like
the nearest neighbor rule with city-block or Euclidean dis-
tances. They are also more robust to normalization effects
and using better models for the features improves the re-
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